Papers for

privacy officers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Using app activity logs reveals more than screen time in smartphone use

Beyond Screen Time: Demonstrating the Value of App Activity Logs to Understand User Behavior Further

Abstract: Research on smartphone use remains fragmented, with studies differing in measures, methods, and platforms, limiting what we know about everyday behavior. We argue that event-level app activity logs should form the backbone of research on actual rather than recalled smartphone use. We conduct a secondary analysis of 4,571,252 app events from 1,972 participants across three longitudinal cohorts differing in age, country, recruitment, and platform, complemented by a published reference cohort. We reproduce established aggregate and micro-usage measures and examine temporal structure, application composition, transitions, and individual distinctiveness. Aggregate usage varies less than the organization of activity: adolescent use, for example, is structured around school schedules, while other cohorts show weaker within-day patterns. Application and transition patterns reveal behavioral differences obscured by screen time; an intervention reduced daily usage while increasing mean session duration. Activity traces also enabled 15-22% top-1 participant re-identification. We discuss methodological, reproducibility, and privacy implications for HCI.

Mon 21 SeptHuman-Computer Interaction
The gist
Measuring how long people use their smartphones by just looking at total screen time misses important details. The authors show that detailed records of app use—like which apps are opened when and how users switch between them—give a richer picture of behavior. For example, teenagers use apps around their school schedule, a pattern hidden by simple screen time counts. This detailed approach also risks identifying individual users from their app use patterns. The authors highlight how these findings affect research methods and privacy.
Open → 2609.24477v1

Privacy risks from detailed dot maps reveal exact locations

Privacy Leakage from a Thousand Words: Millipixel Location Recovery from Dot Maps

Abstract: Dot maps, which visualize individual data points as dots over a geographic region, are widely used across diverse domains to represent spatial patterns in sensitive data. However, the understanding of the privacy risks associated with dot maps remains limited, particularly for maps covering large geographic areas. In this paper, we systematically analyze these risks and present AutoLocate, an automated framework for high-precision location recovery. At its core, AutoLocate exploits anti-aliasing artifacts introduced during map rendering, which inadvertently encode sub-pixel information about dot locations. AutoLocate formulates location recovery as a black-box optimization problem, iteratively refining estimated coordinates by minimizing perceptual discrepancies over these artifacts between the target map and rendered candidate maps. Extensive experiments on both real-world and synthetic datasets, across different attack scenarios and a broad range of map configurations (e.g., map scale, background, resolution), demonstrate the effectiveness of AutoLocate. In particular, it achieves average recovery errors as low as 1 meter (approximately 0.0002 pixel precision) on small-scale maps of the United States, over 200x more accurate than existing approaches. We also propose mitigation strategies and introduce a privacy risk assessment tool to help practitioners evaluate and reduce privacy leakage when publishing dot maps.

Mon 7 SeptCryptography and SecurityComputer Vision and Pattern Recognition
The gist
Dot maps show individual points on a map and are often used to display sensitive data. The authors found that these maps unintentionally leak very precise location information because of tiny visual effects caused when the map is drawn. They created a tool called AutoLocate that can figure out exact locations from these maps with remarkable accuracy, much better than previous methods. They also suggest ways to reduce these leaks and help people check how risky their dot maps are before sharing them.
Open → 2609.07623v1